The National Highway Transportation Safety Administration (NHTSA), Federal Motor Carrier Safety Administration (FMCSA), the American Truck Association (ATA), safety advocates, and transportation researchers have all identified driver fatigue as a high priority commercial motor vehicle (CMV) safety issue, just as it proves to be in the use of personal transportation. Driver fatigue manifests itself in drowsiness, a state of diminished mental alertness, in turn impairing an individual's ability to operate a vehicle safely and, thereby, increasing the risk of human error that could lead to fatalities and injuries.
Drowsiness is the body's reaction to fatigue and drowsiness slows reaction time, decreases awareness, and impairs judgment. Such studies as have been done show that drivers tend to underreport drowsiness, either because they are not aware of being drowsy or underestimate the extent to which they are impaired by drowsiness. Incidence of impairing drowsiness is underestimated because it is so difficult to quantify and measure.
Anecdotally, most drivers are aware of the existence but not of the extent of the impairing effects of drowsiness on their driving. Obtaining, however, reliable data on fatigue-related crashes is challenging. Good numbers on how many collisions are caused by drowsiness are difficult to ascertain, but are felt to be a significant contributor to the number of crashes. Thus, the science of determining root causes is robbed of its most significant tool, direct observation of cause and effect. For instance, if a motorist is unharmed in a crash, the increased arousal following the incident usually masks the impairment that could assist investigating officers in attributing the crash to drowsiness. As a result, while drowsiness may well be a contributing factor in many motor vehicle crashes, it is underreported in databases that contain police accident reports.
An investigator may report that a crash was caused by a driver running a red light, whereas the real cause of the crash lies upstream, in that the driver was not appropriately vigilant to notice the redness of the light, due to his or her state of drowsiness and fatigue. There exists no currently uniform method of testing for drowsiness across significant populations of drivers.
Vehicle-based operator alertness or, conversely, drowsiness monitoring technologies exemplify the most common approaches currently used to monitor driver fatigue. Generally such technologies are enabled by monitoring one or another operator behavior or physiological attributes such as eye gaze, eye closure, pupil occlusion, head position and movement, brain wave activity, heart rate, and other such measurable physiological attributes. Again, however, because the use of any specific test is not widely available, there have not been tests which determine the specific metrics of any of these attributes as a bellwether of drowsiness likely to lead to a collision. Only when there exist a significant number of such installations can overarching data be developed. There are other problems, as well, with existing drowsiness detection systems for vehicles:                Available drowsiness detection systems do not work well in all driving conditions.        Available drowsiness detection systems suffer from the problem of false positives and false negatives. A large percentage of test reports on drowsiness sensors are devoted to assessing when the driver was actually drowsy. The reality is that presently available drowsiness detection systems cannot be totally certain if a driver is drowsy or alert.        Conventional technologies currently used for drowsiness detection are prohibitively expensive, affirmatively preventing widespread adoption.        Drivers do not like to have additional devices to interact with or to be distracted from the task of driving, especially during times of high stress. In addition to driver irritation, any additional task that require hand movement away from the task of steering or eye movement away from the roadway is a safety issue.        
Existing drowsiness sensors are prone to false positive and false negative warning thresholds. False positive and false negative warnings also means triggers of unnecessary alarms or alertness measures in response to those warnings which imparts uncertainty and suggests to drivers that the sensors are unnecessary at best and, more likely, annoying. Before definitive action to direct the driver's attention to the driving task or even before meaningful statistics can be garnered, knowing if the driver is truly drowsy is necessary. For this reason, verification of sensed drowsiness is a necessary attribute of any such system of detection and accident prevention.
To date, there has been no system that suitably both measures and correlates physiological attributes across large segments of the population, and, then, verifies the presence of those attributes as reflecting actual drowsiness and its attendant impairment. There is no system that is sufficiently efficacious and also inexpensive enough to become a candidate for widespread implementation and study. What is needed in the art is a method and an apparatus for reliable detection of attributes of drowsiness-based impairment and then verification of impairment based upon the actual state of drowsiness after detection.